References & Scientific Basis 

This tool is built upon peer-reviewed scientific research and open-source technologies.

1. Primary Scientific Paper

The predictive model used in this tool is based on the algorithms and data presented in:

Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., ... & Root, D. E. (2016). Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology, 34(2), 184-191. https://doi.org/10.1038/nbt.3437

2. Dataset Source

The training data was derived from the Microsoft Research Azimuth Project, which provides "Rule Set 2" for CRISPR-Cas9 on-target efficiency prediction.

Microsoft Research Azimuth GitHub Repository: https://github.com/MicrosoftResearch/Azimuth

3. Technologies Used

• TensorFlow.js: For client-side machine learning inference.

• Python (Pandas & Keras): For data preprocessing and model training.